
import pandas as pd
import seaborn as sns
import plotly.express as px
from copy import copy
import matplotlib.pyplot as plt
import numpy as np
import plotly.figure_factory as ff
import plotly.graph_objects as go


from jupyterthemes import jtplot# Jupyter theme
import jupyterthemes as jt 
from jupyterthemes.stylefx import set_nb_theme

!jt -t monokai
#jtplot.style(theme = 'monokai', context= 'notebook', ticks= True, grid= False)


!jt -t monokai


# Read the stock data file

stocks_df= pd.read_csv('stocks_dataset.csv')
stocks_df


# Getting dataframe info
stocks_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1699 entries, 0 to 1698
Data columns (total 9 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Date    1699 non-null   object 
 1   FB      1699 non-null   float64
 2   TWTR    1699 non-null   float64
 3   NFLX    1699 non-null   float64
 4   BA      1699 non-null   float64
 5   T       1699 non-null   float64
 6   MGM     1699 non-null   float64
 7   TSLA    1699 non-null   float64
 8   sp500   1699 non-null   float64
dtypes: float64(8), object(1)
memory usage: 119.6+ KB


stocks_df.describe()


stocks_df.max()

Date        9/9/2019
FB        268.440002
TWTR       73.309998
NFLX       548.72998
BA        440.619995
T          43.470001
MGM        38.029999
TSLA          1643.0
sp500    3386.149902
dtype: object


stocks_df


stocks_df.columns[1:]

Index(['FB', 'TWTR', 'NFLX', 'BA', 'T', 'MGM', 'TSLA', 'sp500'], dtype='object')


# Function to normalize the prices based on the initial price
def normalize(df):
    x = df.copy()
    for i in x.columns[1:]:
        x[i] = x[i]/x[i][0]
    return x


normalize(stocks_df)


# 10.595087 * 1000 = $10595.087


stocks_df.head()


# Function to plot interactive plot
def interactive_plot(df, title):
    fig = px.line(title = title)
    for i in df.columns[1:]:
        fig.add_scatter(x = df['Date'], y = df[i], name =i)

    fig.show()


# Plot interactive chart
interactive_plot(stocks_df, 'Prices')


# Plot normalized interactive chart
interactive_plot(normalize(stocks_df), 'NOrmalized Prices')


# $63.77*1000 = $63770.00


stocks_df


# Function to calculate the daily returns 
def daily_return(df):
    df_daily_return = df.copy()
    
    for i in df.columns[1:]:
        for j in range(1, len(df)):
            df_daily_return[i][j] = (( df[i][j] - df[i][j-1]) / df[i][j-1]) * 100
        df_daily_return[i][0]=0
    return df_daily_return


stocks_daily_return = daily_return(stocks_df)
stocks_daily_return


# Select any stock, let's say Apple or Tesla
stocks_daily_return['TSLA']

0       0.000000
1      -1.302144
2       4.893077
3      -4.768482
4       0.653116
          ...   
1694    3.790991
1695    0.134680
1696   -0.133153
1697    0.307062
1698   -2.475194
Name: TSLA, Length: 1699, dtype: float64


# Select the S&P500 (Market)
stocks_daily_return['sp500']

0       0.000000
1       1.342756
2       0.072293
3      -0.237039
4       0.809534
          ...   
1694    0.718102
1695    0.361193
1696    0.642974
1697    0.642770
1698    0.063303
Name: sp500, Length: 1699, dtype: float64


# plot a scatter plot between the selected stock and the S&P500 (Market)
stocks_daily_return.plot(kind= 'scatter', x = 'sp500', y='TSLA', color='w')

<AxesSubplot:xlabel='sp500', ylabel='TSLA'>


# Fit a polynomial between the selected stock and the S&P500 (Poly with order = 1 is a straight line)

# beta represents the slope of the line regression line (market return vs. stock return). 
# Beta is a measure of the volatility or systematic risk of a security or portfolio compared to the entire market (S&P500) 
# Beta is used in the CAPM and describes the relationship between systematic risk and expected return for assets 

# Beta = 1.0, this indicates that its price activity is strongly correlated with the market. 
# Beta < 1, indicates that the security is theoretically less volatile than the market (Ex: Utility stocks). If the stock is included, this will make the portfolio less risky compared to the same portfolio without the stock.
# Beta > 1, indicates that the security's price is more volatile than the market. For instance, Tesla stock beta is 1.26 indicating that it's 26% more volatile than the market. 
# Tech stocks generally have higher betas than S&P500 but they also have excess returns
# MGM is 65% more volatile than the S&P500!

#1 is the order of the polynomial ie. that represents a straight line
beta, alpha = np.polyfit(stocks_daily_return['sp500'], stocks_daily_return['TSLA'], 1)
print('Beta for {} stock is = {} and alpha is = {}'.format('TSLA', beta, alpha))

Beta for TSLA stock is = 1.2359723602006574 and alpha is = 0.13716200993236882


# Now let's plot the scatter plot and the straight line on one plot
stocks_daily_return.plot(kind = 'scatter', x = 'sp500', y = 'TSLA', color = 'w')

# Straight line equation with alpha and beta parameters 
# Straight line equation is y = beta * rm + alpha
plt.plot(stocks_daily_return['sp500'], beta * stocks_daily_return['sp500'] + alpha, '-', color = 'r')

[<matplotlib.lines.Line2D at 0x17296ca2310>]


# Fit a polynomial between the selected stock and the S&P500 (Poly with order = 1 is a straight line)

# beta represents the slope of the line regression line (market return vs. stock return). 
# Beta is a measure of the volatility or systematic risk of a security or portfolio compared to the entire market (S&P500) 
# Beta is used in the CAPM and describes the relationship between systematic risk and expected return for assets 

# Beta = 1.0, this indicates that its price activity is strongly correlated with the market. 
# Beta < 1, indicates that the security is theoretically less volatile than the market (Ex: Utility stocks). If the stock is included, this will make the portfolio less risky compared to the same portfolio without the stock.
# Beta > 1, indicates that the security's price is more volatile than the market. For instance, Tesla stock beta is 1.26 indicating that it's 26% more volatile than the market. 
# Tech stocks generally have higher betas than S&P500 but they also have excess returns
# MGM is 65% more volatile than the S&P500!

#1 is the order of the polynomial ie. that represents a straight line
beta, alpha = np.polyfit(stocks_daily_return['sp500'], stocks_daily_return['MGM'], 1)
print('Beta for {} stock is = {} and alpha is = {}'.format('MGM', beta, alpha))

Beta for MGM stock is = 1.651712414792891 and alpha is = -0.030189775264089107


# Now let's plot the scatter plot and the straight line on one plot
stocks_daily_return.plot(kind = 'scatter', x = 'sp500', y = 'MGM', color = 'w')

# Straight line equation with alpha and beta parameters 
# Straight line equation is y = beta * rm + alpha
plt.plot(stocks_daily_return['sp500'], beta * stocks_daily_return['sp500'] + alpha, '-', color = 'r')

[<matplotlib.lines.Line2D at 0x172970b2bb0>]


beta

1.2359723602006574


# Let's calculate the average daily rate of return for S&P500
stocks_daily_return['sp500'].mean()

0.04457361768265508


# Let's calculate the annualized rate of return for S&P500 
# Note that out of 365 days/year, stock exchanges are closed for 104 days during weekend days (Saturday and Sunday) 
# Check your answers with: https://dqydj.com/sp-500-return-calculator/
#252 total market days based in US differs in different regions

rm=stocks_daily_return['sp500'].mean() *252
rm

11.23255165602908


# Assume risk free rate is zero
# Also you can use the yield of a 10-years U.S. Government bond as a risk free rate
rf =0 # approximation would not be zero in real world
# Calculate return for any security (APPL) using CAPM  
ER_TSLA = rf + (beta * (rm-rf))


ER_TSLA

13.883123381378066


beta, alpha = np.polyfit(stocks_daily_return['sp500'], stocks_daily_return['BA'], 1)
print('Beta for {} stock is = {}'.format('BA', beta))

Beta for BA stock is = 1.4304608163084236


# Let's create a placeholder for all betas and alphas (empty dictionaries)
beta = {}
alpha = {}

# Loop on every stock daily return
for i in stocks_daily_return.columns:

  # Ignoring the date and S&P500 Columns 
  if i != 'Date' and i != 'sp500':
    # plot a scatter plot between each individual stock and the S&P500 (Market)
    stocks_daily_return.plot(kind = 'scatter', x = 'sp500', y = i, color = 'w')
    
    # Fit a polynomial between each stock and the S&P500 (Poly with order = 1 is a straight line)
    b, a = np.polyfit(stocks_daily_return['sp500'], stocks_daily_return[i], 1)
    
    plt.plot(stocks_daily_return['sp500'], b * stocks_daily_return['sp500'] + a, '-', color = 'r')
    
    beta[i] = b
    
    alpha[i] = a
    
    plt.show()


# Let's view Beta for every stock 
beta

{'FB': 1.0968490880203263,
 'TWTR': 1.1723596183159952,
 'NFLX': 1.0445724485449472,
 'BA': 1.4304608163084236,
 'T': 0.7523861924715606,
 'MGM': 1.651712414792891,
 'TSLA': 1.2359723602006574}


alpha

{'FB': 0.0736431516462632,
 'TWTR': -0.003852540866356543,
 'NFLX': 0.12722576030846006,
 'BA': -0.01837530706924207,
 'T': -0.03378962624015625,
 'MGM': -0.030189775264089107,
 'TSLA': 0.13716200993236882}


# Let's do the same plots but in an interactive way
# Explore some wierd points in the dataset: Tesla stock return was at 24% when the S&P500 return was -0.3%!

for i in stocks_daily_return.columns:
  
  if i != 'Date' and i != 'sp500':
    
    # Use plotly express to plot the scatter plot for every stock vs. the S&P500
    fig = px.scatter(stocks_daily_return, x = 'sp500', y = i, title = i)

    # Fit a straight line to the data and obtain beta and alpha
    b, a = np.polyfit(stocks_daily_return['sp500'], stocks_daily_return[i], 1)
    
    # Plot the straight line 
    fig.add_scatter(x = stocks_daily_return['sp500'], y = b*stocks_daily_return['sp500'] + a)
    fig.show()

Beta for BA stock is = 1.4304608163084236


stocks_df.describe()


stocks_df.max()


# Account balance on Aug 7th, 2020 = 10.595087 * $1000 = $10,595.087


# Facebook stock dropped from $209.78 on Jan 2nd, 2020 to $146.01 by March 16th, 2020 
# Net loss = $209.78 - $146.01 = $63.77 * 1000 = $63770


# Get the daily returns 
stocks_daily_return = daily_return(stocks_df)
stocks_daily_return


# Fit a polynomial between the selected stock and the S&P500 (Poly with order = 1 is a straight line)
beta, alpha = np.polyfit(stocks_daily_return['sp500'], stocks_daily_return['MGM'], 1)
print(beta)  
# Now let's plot the scatter plot and the straight line on one plot
stocks_daily_return.plot(kind = 'scatter', x = 'sp500', y = 'MGM', color = 'w')

# Straight line equation with alpha and beta parameters 
# Straight line equation is y = beta * rm + alpha
plt.plot(stocks_daily_return['sp500'], beta * stocks_daily_return['sp500'] + alpha, '-', color = 'r');


beta, alpha = np.polyfit(stocks_daily_return['sp500'], stocks_daily_return['BA'], 1)
print('Beta for {} stock is = {}'.format('BA', beta))


# Calculate return for BA using CAPM  
ER_BA = rf + ( beta * (rm - rf) ) 
print(ER_BA)

16.067725011109893

	Date	FB	TWTR	NFLX	BA	T	MGM	TSLA	sp500
0	11/7/2013	47.560001	44.900002	46.694286	131.509995	35.110001	18.580000	139.770004	1747.150024
1	11/8/2013	47.529999	41.650002	47.842857	133.490005	35.169998	19.090000	137.949997	1770.609985
2	11/11/2013	46.200001	42.900002	48.272858	132.529999	35.029999	19.200001	144.699997	1771.890015
3	11/12/2013	46.610001	41.900002	47.675713	132.330002	35.169998	19.260000	137.800003	1767.689941
4	11/13/2013	48.709999	42.599998	47.897144	133.169998	35.070000	19.700001	138.699997	1782.000000
...	...	...	...	...	...	...	...	...	...
1694	8/3/2020	251.960007	36.389999	498.619995	162.270004	29.620001	16.320000	1485.000000	3294.610107
1695	8/4/2020	249.830002	36.349998	509.640015	165.070007	30.010000	16.719999	1487.000000	3306.510010
1696	8/5/2020	249.119995	36.790001	502.109985	174.279999	29.850000	16.719999	1485.020020	3327.770020
1697	8/6/2020	265.279999	37.689999	509.079987	172.199997	29.840000	18.459999	1489.579956	3349.159912
1698	8/7/2020	268.440002	37.139999	494.730011	170.020004	30.020000	19.030001	1452.709961	3351.280029

	FB	TWTR	NFLX	BA	T	MGM	TSLA	sp500
count	1699.000000	1699.000000	1699.000000	1699.000000	1699.000000	1699.000000	1699.000000	1699.000000
mean	135.966863	31.296698	195.423654	218.325633	35.296986	25.720865	311.299388	2413.219428
std	50.062473	11.508628	127.700712	98.968191	3.412142	5.200605	203.097452	424.825240
min	44.820000	14.010000	44.887142	95.010002	26.770000	7.140000	120.500000	1741.890015
25%	88.080002	19.600001	90.474998	132.840004	32.790001	21.935001	218.829994	2053.294921
50%	136.759995	31.610001	143.830002	158.289993	34.900002	25.879999	253.740005	2360.159912
75%	178.075005	38.229999	320.024993	334.360001	38.004999	29.250000	323.565002	2783.330078
max	268.440002	73.309998	548.729980	440.619995	43.470001	38.029999	1643.000000	3386.149902

	Date	FB	TWTR	NFLX	BA	T	MGM	TSLA	sp500
0	11/7/2013	47.560001	44.900002	46.694286	131.509995	35.110001	18.580000	139.770004	1747.150024
1	11/8/2013	47.529999	41.650002	47.842857	133.490005	35.169998	19.090000	137.949997	1770.609985
2	11/11/2013	46.200001	42.900002	48.272858	132.529999	35.029999	19.200001	144.699997	1771.890015
3	11/12/2013	46.610001	41.900002	47.675713	132.330002	35.169998	19.260000	137.800003	1767.689941
4	11/13/2013	48.709999	42.599998	47.897144	133.169998	35.070000	19.700001	138.699997	1782.000000
...	...	...	...	...	...	...	...	...	...
1694	8/3/2020	251.960007	36.389999	498.619995	162.270004	29.620001	16.320000	1485.000000	3294.610107
1695	8/4/2020	249.830002	36.349998	509.640015	165.070007	30.010000	16.719999	1487.000000	3306.510010
1696	8/5/2020	249.119995	36.790001	502.109985	174.279999	29.850000	16.719999	1485.020020	3327.770020
1697	8/6/2020	265.279999	37.689999	509.079987	172.199997	29.840000	18.459999	1489.579956	3349.159912
1698	8/7/2020	268.440002	37.139999	494.730011	170.020004	30.020000	19.030001	1452.709961	3351.280029

	Date	FB	TWTR	NFLX	BA	T	MGM	TSLA	sp500
0	11/7/2013	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	11/8/2013	0.999369	0.927617	1.024598	1.015056	1.001709	1.027449	0.986979	1.013428
2	11/11/2013	0.971405	0.955457	1.033807	1.007756	0.997721	1.033369	1.035272	1.014160
3	11/12/2013	0.980025	0.933185	1.021018	1.006235	1.001709	1.036598	0.985905	1.011756
4	11/13/2013	1.024180	0.948775	1.025760	1.012623	0.998861	1.060280	0.992345	1.019947
...	...	...	...	...	...	...	...	...	...
1694	8/3/2020	5.297729	0.810468	10.678394	1.233899	0.843634	0.878364	10.624597	1.885705
1695	8/4/2020	5.252944	0.809577	10.914398	1.255190	0.854742	0.899892	10.638906	1.892516
1696	8/5/2020	5.238015	0.819376	10.753136	1.325222	0.850185	0.899892	10.624740	1.904685
1697	8/6/2020	5.577796	0.839421	10.902404	1.309406	0.849900	0.993541	10.657365	1.916927
1698	8/7/2020	5.644239	0.827171	10.595087	1.292830	0.855027	1.024220	10.393575	1.918141

	Date	FB	TWTR	NFLX	BA	T	MGM	TSLA	sp500
0	11/7/2013	47.560001	44.900002	46.694286	131.509995	35.110001	18.580000	139.770004	1747.150024
1	11/8/2013	47.529999	41.650002	47.842857	133.490005	35.169998	19.090000	137.949997	1770.609985
2	11/11/2013	46.200001	42.900002	48.272858	132.529999	35.029999	19.200001	144.699997	1771.890015
3	11/12/2013	46.610001	41.900002	47.675713	132.330002	35.169998	19.260000	137.800003	1767.689941
4	11/13/2013	48.709999	42.599998	47.897144	133.169998	35.070000	19.700001	138.699997	1782.000000

TASK #1: UNDERSTAND THE THEORY AND INTUITION BEHIND CAPITAL ASSET PRICING MODEL (CAPM)¶

TASK #2: IMPORT LIBRARIES/DATASETS AND PERFORM EXPLORATORY DATA ANALYSIS¶

TASK #3: PERFORM DATA SCALING¶

TASK #4: PERFORM DATA VISUALIZATION¶

TASK #5: DEVELOP A FUNCTION TO CALCULATE DAILY RETURNS¶

TASK #6: CALCULATE BETA FOR TESLA STOCK¶

TASK #7: APPLY THE CAPM FORMULA TO AN INDIVIDUAL STOCK¶

TASK #8: CALCULATE BETA FOR ALL STOCKS¶

AWESOME JOB!¶

MINI CHALLENGES SOLUTIONS¶

	Date	FB	TWTR	NFLX	BA	T	MGM	TSLA	sp500
0	11/7/2013	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
1	11/8/2013	-0.063082	-7.238307	2.459768	1.505597	0.170883	2.744887	-1.302144	1.342756
2	11/11/2013	-2.798229	3.001200	0.898778	-0.719159	-0.398064	0.576223	4.893077	0.072293
3	11/12/2013	0.887446	-2.331002	-1.237020	-0.150907	0.399655	0.312495	-4.768482	-0.237039
4	11/13/2013	4.505467	1.670635	0.464452	0.634774	-0.284328	2.284533	0.653116	0.809534
...	...	...	...	...	...	...	...	...	...
1694	8/3/2020	-0.674101	-0.027481	1.992307	2.702534	0.135230	1.429459	3.790991	0.718102
1695	8/4/2020	-0.845374	-0.109923	2.210104	1.725521	1.316674	2.450974	0.134680	0.361193
1696	8/5/2020	-0.284196	1.210462	-1.477519	5.579446	-0.533156	0.000000	-0.133153	0.642974
1697	8/6/2020	6.486835	2.446311	1.388142	-1.193483	-0.033501	10.406699	0.307062	0.642770
1698	8/7/2020	1.191195	-1.459273	-2.818806	-1.265966	0.603217	3.087768	-2.475194	0.063303